# Deep Spectral CNN for Laser Induced Breakdown Spectroscopy

arXiv: https://arxiv.org/abs/2012.01653 · HTML: https://mlsanity.com/paper/2012.01653/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Semi-supervised Learning of Pushforwards For Domain Translation & Adaptation](https://mlsanity.com/paper/2304.08673/) (2023)
- [Neural density estimation and uncertainty quantification for laser induced breakdown spectroscopy spectra](https://mlsanity.com/paper/2108.08709/) (2021)
- [SpectroscopyNet: Learning to pre-process Spectroscopy Signals without clean data](https://mlsanity.com/paper/2110.13748/) (2021)
- [Generative structured normalizing flow Gaussian processes applied to spectroscopic data](https://mlsanity.com/paper/2212.07554/) (2022)
- [Representation Disentaglement via Regularization by Causal Identification](https://mlsanity.com/paper/2303.00128/) (2023)

## Abstract (arXiv)

This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, 'Curiosity'.